title: On high-resolution image estimation using low-resolution brain MRI.
abstract: In the context of medical imaging, super-resolution (SR) is currently a promising post-processing technique to increase the image resolution. However, although many SR methods have been proposed in the literature, the gain of this type of approach in a real situation has not been precisely quantified. In this work, we evaluate image acquisition protocols and SR algorithms using in-vivo brain MR data as gold standard. The results show that using orthogonal image acquisition protocols lead to better reconstructed images than overlapping parallel low-resolution image stacks. Moreover, if the preprocessing steps (such as image denoising and intensity correction) are carefully performed, there is no significant differences between the evaluated SR algorithms.
authors.forename: François
authors.initials: F
authors.lastname: Rousseau
authors.forename: Daniel
authors.initials: D
authors.lastname: Gounot
authors.forename: Colin
authors.initials: C
authors.lastname: Studholme
mesh_terms: D000465:Algorithms; D001921:Brain / Q000033:anatomy & histology*; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008279:Magnetic Resonance Imaging; D059629:Signal-To-Noise Ratio